This is a brief presentation on Meta analysis using the example based presentation method.

1. Systematic Review & Meta-
analysis
Ahmad Abed Ahmadi BSPH, MSc

5. Types of Review
• Narrative review
• Systematic review
• Scoping review
• Rapid review
• Cochrane systematic review
• Meta analysis
• Network meta analysis
• Individual patient data (IPD) meta analysis

6. Types of Review
• Systematic review (Qualitative summary)
• Meta-analysis (Quantitative summary)

7. How to conduct a review

8. Why bother
• Good questions
• Timely topics
• Topics with multiple studies
• Topics with conflicting results

9. Formulating the question
• Specific and spliced
• Don’t take a random question
• The question doesn’t need to be complex
• You don’t need to always be first
• You should select a topic of your interest
• Use the PICO

10. Research Question
How does a social history of smoking in
patients diagnosed with COVID-19 affect
disease severity?
P- COVID-19 patients who are smokers
I- Active smoking in COVID-19 patients
C- COVID-19 patients who are non-smokers
O- Disease progression, intensive care unit admission, need for
mechanical ventilation, and mortality

11. Draft your research strategy
• Question
• How does a social history of smoking in patients diagnosed
with COVID-19 affect disease severity?
• Keywords
• smoking status
• COVID‐19
• Inclusion
• Randomized, and observational studies reporting the smoking status of
hospitalized patients presenting with different severities of disease and/or at
least one clinical endpoint of interest.

12. Draft your research strategy
• Exclusion
• Studies on other coronaviruses or if there was insufficient information to
distinguish disease severity based on smoking status.
• Case series involving less than 20 patients, review articles, editorials,
conference abstracts, and nonclinical studies as well as Preprints.
• Search limits
• No language limitations

13. Register your study
• Register in Prospero

14. Performing the review
• Be systematic in your search
• Source of articles
• Pubmed (medline)
• EMBASE
• Cochrane central register of trials
• Other topic specific
• Grey literature (google scholar)
• Reference section of articles

15. Searching for articles
• Controlled vocabulary
• MeSH headings – for medline
• Embase - Emtree

17. Screen search results
1038
688
350
315
35
12
47

18. Tools to stay organized
• Rayyan
• Covidence
• Revman
• Cochrane

20. Quality assessment
• Observational studies
• Newcastle Ottawa scales (better if 2 people do it independitly)
• Robins-1
• Randomized trials
• Cochrane tool

22. When to meta analysis
• Multiple high quality studies with:
• Similar design
• Population
• Comparator(s), and
• Similar outcomes

23. When to not meta analyze
• Studies or (study outcomes) are too heterogeneous
• Too few studies available
• Studies are too low quality

24. What does meta analysis gives you
• Graphical display to:
• Illustrate the strength of effects among studies
• Examine uncertainty around individual studies
• Visually apprise evidence of heterogeneity
• Report combined effect of the individual studies
• Concisely summarize strength of effect, precision and heterogeneity

25. Estimating a summary measure (Pooled
effect)
Weighting options:
• Fixed effects
• Random effects

26. Types of between study heterogeneity
• Clinical
• Participant characteristics
• Exposures/interventions
• outcomes
• Methodological
• Study design
• Study quality
• Statistical

28. Forest plot
showing
the effect
of current
smoking
on severe
COVID‐19

29. Forest plot
showing
the effect
of current
smoking
on severe
or critical
COVID‐19

30. Forest plot
showing
the effect
of current
smoking on
mortality

32. Forest plot
showing the
effect of a
smoking
history on
severe
COVID‐19

33. Forest plot
showing the
effect of a
smoking
history on
severe or
critical
COVID‐19

34. Forest plot
showing
the effect
of a
smoking
history on
mortality

36. Advantages of meta-analysis
• Results can be generalized to a larger population
• The precision and accuracy of estimates can be improved as more
data is used. This, in turn, may increase the statistical power to detect
an effect.
• Inconsistency of results across studies can be quantified and
analyzed. For instance, does inconsistency arise from sampling error,
or are study results (partially) influenced by between-study
heterogeneity.
• Hypothesis testing can be applied on summary estimates,
• Moderators can be included to explain variation between studies

37. Pitfalls
• A meta-analysis of several small studies does not predict the results of a
single large study.
• Some have argued that a weakness of the method is that sources of bias
are not controlled by the method:
• a good meta-analysis of badly designed studies will still result in bad
statistics
• this would mean that only methodologically sound studies should be
included in a meta-analysis, a practice called 'best evidence synthesis'.
• Other meta-analysts would include weaker studies, and add a study-level
predictor variable that reflects the methodological quality of the studies to
examine the effect of study quality on the effect size.

38. Publication bias: the file drawer problem
• Another potential pitfall is the reliance on the available corpus of
published studies, which may create exaggerated outcomes due to
publication bias, as studies which show negative results or
insignificant results are less likely to be published.
• For example, one may have overlooked dissertation studies or studies
that have never been published.
• This is not easily solved, as one cannot know how many studies have
gone unreported.
• The distribution of effect sizes can be visualized with a funnel plot
which is a scatter plot of sample size and effect sizes.

39. Agenda-driven bias
• The most severe fault in meta-analysis often occurs when the person
or persons doing the meta-analysis have an economic, social, or
political agenda such as the passage or defeat of legislation.
• People with these types of agendas may be more likely to abuse
meta-analysis due to personal bias.
• For example, researchers favorable to the author's agenda are likely
to have their studies cherry-picked while those not favorable will be
ignored or labeled as "not credible".
• In addition, the favored authors may themselves be biased or paid to
produce results that support their overall political, social, or economic
goals in ways such as selecting small favorable data sets and not
incorporating larger unfavorable data sets.
• The influence of such biases on the results of a meta-analysis is
possible because the methodology of meta-analysis is highly
malleable

41. ‫تشکر‬
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